Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Sunday 07 February 2021
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 428,473 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 428,463 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
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## 70 2020-05-09 East of England 29
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## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
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## 87 2020-05-26 East of England 14
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## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
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## 125 2020-07-03 East of England 0
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## 128 2020-07-06 East of England 2
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## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 8
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
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## 139 2020-07-17 East of England 0
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## 143 2020-07-21 East of England 1
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## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 2
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
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## 199 2020-09-15 East of England 0
## 200 2020-09-16 East of England 0
## 201 2020-09-17 East of England 0
## 202 2020-09-18 East of England 0
## 203 2020-09-19 East of England 0
## 204 2020-09-20 East of England 2
## 205 2020-09-21 East of England 0
## 206 2020-09-22 East of England 2
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## 208 2020-09-24 East of England 0
## 209 2020-09-25 East of England 1
## 210 2020-09-26 East of England 1
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## 212 2020-09-28 East of England 2
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## 243 2020-10-29 East of England 10
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## 247 2020-11-02 East of England 9
## 248 2020-11-03 East of England 14
## 249 2020-11-04 East of England 11
## 250 2020-11-05 East of England 12
## 251 2020-11-06 East of England 18
## 252 2020-11-07 East of England 10
## 253 2020-11-08 East of England 13
## 254 2020-11-09 East of England 16
## 255 2020-11-10 East of England 26
## 256 2020-11-11 East of England 14
## 257 2020-11-12 East of England 14
## 258 2020-11-13 East of England 21
## 259 2020-11-14 East of England 19
## 260 2020-11-15 East of England 13
## 261 2020-11-16 East of England 11
## 262 2020-11-17 East of England 17
## 263 2020-11-18 East of England 19
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## 270 2020-11-25 East of England 19
## 271 2020-11-26 East of England 19
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## 1760 2020-04-19 South East 70
## 1761 2020-04-20 South East 87
## 1762 2020-04-21 South East 51
## 1763 2020-04-22 South East 54
## 1764 2020-04-23 South East 57
## 1765 2020-04-24 South East 64
## 1766 2020-04-25 South East 51
## 1767 2020-04-26 South East 51
## 1768 2020-04-27 South East 41
## 1769 2020-04-28 South East 40
## 1770 2020-04-29 South East 47
## 1771 2020-04-30 South East 29
## 1772 2020-05-01 South East 37
## 1773 2020-05-02 South East 36
## 1774 2020-05-03 South East 17
## 1775 2020-05-04 South East 35
## 1776 2020-05-05 South East 29
## 1777 2020-05-06 South East 25
## 1778 2020-05-07 South East 27
## 1779 2020-05-08 South East 26
## 1780 2020-05-09 South East 28
## 1781 2020-05-10 South East 19
## 1782 2020-05-11 South East 25
## 1783 2020-05-12 South East 27
## 1784 2020-05-13 South East 18
## 1785 2020-05-14 South East 32
## 1786 2020-05-15 South East 25
## 1787 2020-05-16 South East 22
## 1788 2020-05-17 South East 18
## 1789 2020-05-18 South East 23
## 1790 2020-05-19 South East 12
## 1791 2020-05-20 South East 22
## 1792 2020-05-21 South East 15
## 1793 2020-05-22 South East 17
## 1794 2020-05-23 South East 21
## 1795 2020-05-24 South East 17
## 1796 2020-05-25 South East 13
## 1797 2020-05-26 South East 19
## 1798 2020-05-27 South East 19
## 1799 2020-05-28 South East 12
## 1800 2020-05-29 South East 22
## 1801 2020-05-30 South East 8
## 1802 2020-05-31 South East 12
## 1803 2020-06-01 South East 11
## 1804 2020-06-02 South East 13
## 1805 2020-06-03 South East 18
## 1806 2020-06-04 South East 11
## 1807 2020-06-05 South East 11
## 1808 2020-06-06 South East 10
## 1809 2020-06-07 South East 12
## 1810 2020-06-08 South East 8
## 1811 2020-06-09 South East 11
## 1812 2020-06-10 South East 11
## 1813 2020-06-11 South East 5
## 1814 2020-06-12 South East 6
## 1815 2020-06-13 South East 7
## 1816 2020-06-14 South East 7
## 1817 2020-06-15 South East 8
## 1818 2020-06-16 South East 14
## 1819 2020-06-17 South East 10
## 1820 2020-06-18 South East 4
## 1821 2020-06-19 South East 7
## 1822 2020-06-20 South East 5
## 1823 2020-06-21 South East 3
## 1824 2020-06-22 South East 2
## 1825 2020-06-23 South East 9
## 1826 2020-06-24 South East 7
## 1827 2020-06-25 South East 5
## 1828 2020-06-26 South East 8
## 1829 2020-06-27 South East 9
## 1830 2020-06-28 South East 6
## 1831 2020-06-29 South East 5
## 1832 2020-06-30 South East 5
## 1833 2020-07-01 South East 2
## 1834 2020-07-02 South East 8
## 1835 2020-07-03 South East 3
## 1836 2020-07-04 South East 6
## 1837 2020-07-05 South East 5
## 1838 2020-07-06 South East 4
## 1839 2020-07-07 South East 6
## 1840 2020-07-08 South East 3
## 1841 2020-07-09 South East 7
## 1842 2020-07-10 South East 3
## 1843 2020-07-11 South East 4
## 1844 2020-07-12 South East 5
## 1845 2020-07-13 South East 5
## 1846 2020-07-14 South East 5
## 1847 2020-07-15 South East 6
## 1848 2020-07-16 South East 3
## 1849 2020-07-17 South East 1
## 1850 2020-07-18 South East 5
## 1851 2020-07-19 South East 2
## 1852 2020-07-20 South East 6
## 1853 2020-07-21 South East 4
## 1854 2020-07-22 South East 2
## 1855 2020-07-23 South East 3
## 1856 2020-07-24 South East 1
## 1857 2020-07-25 South East 1
## 1858 2020-07-26 South East 3
## 1859 2020-07-27 South East 1
## 1860 2020-07-28 South East 3
## 1861 2020-07-29 South East 2
## 1862 2020-07-30 South East 3
## 1863 2020-07-31 South East 1
## 1864 2020-08-01 South East 2
## 1865 2020-08-02 South East 4
## 1866 2020-08-03 South East 0
## 1867 2020-08-04 South East 0
## 1868 2020-08-05 South East 0
## 1869 2020-08-06 South East 2
## 1870 2020-08-07 South East 0
## 1871 2020-08-08 South East 2
## 1872 2020-08-09 South East 0
## 1873 2020-08-10 South East 2
## 1874 2020-08-11 South East 1
## 1875 2020-08-12 South East 1
## 1876 2020-08-13 South East 0
## 1877 2020-08-14 South East 0
## 1878 2020-08-15 South East 2
## 1879 2020-08-16 South East 1
## 1880 2020-08-17 South East 0
## 1881 2020-08-18 South East 2
## 1882 2020-08-19 South East 1
## 1883 2020-08-20 South East 0
## 1884 2020-08-21 South East 0
## 1885 2020-08-22 South East 0
## 1886 2020-08-23 South East 1
## 1887 2020-08-24 South East 0
## 1888 2020-08-25 South East 1
## 1889 2020-08-26 South East 0
## 1890 2020-08-27 South East 1
## 1891 2020-08-28 South East 2
## 1892 2020-08-29 South East 1
## 1893 2020-08-30 South East 0
## 1894 2020-08-31 South East 2
## 1895 2020-09-01 South East 1
## 1896 2020-09-02 South East 1
## 1897 2020-09-03 South East 0
## 1898 2020-09-04 South East 1
## 1899 2020-09-05 South East 0
## 1900 2020-09-06 South East 1
## 1901 2020-09-07 South East 0
## 1902 2020-09-08 South East 0
## 1903 2020-09-09 South East 0
## 1904 2020-09-10 South East 1
## 1905 2020-09-11 South East 1
## 1906 2020-09-12 South East 0
## 1907 2020-09-13 South East 3
## 1908 2020-09-14 South East 1
## 1909 2020-09-15 South East 2
## 1910 2020-09-16 South East 2
## 1911 2020-09-17 South East 3
## 1912 2020-09-18 South East 1
## 1913 2020-09-19 South East 1
## 1914 2020-09-20 South East 0
## 1915 2020-09-21 South East 3
## 1916 2020-09-22 South East 0
## 1917 2020-09-23 South East 2
## 1918 2020-09-24 South East 1
## 1919 2020-09-25 South East 3
## 1920 2020-09-26 South East 2
## 1921 2020-09-27 South East 2
## 1922 2020-09-28 South East 6
## 1923 2020-09-29 South East 3
## 1924 2020-09-30 South East 4
## 1925 2020-10-01 South East 4
## 1926 2020-10-02 South East 2
## 1927 2020-10-03 South East 1
## 1928 2020-10-04 South East 1
## 1929 2020-10-05 South East 2
## 1930 2020-10-06 South East 1
## 1931 2020-10-07 South East 4
## 1932 2020-10-08 South East 1
## 1933 2020-10-09 South East 1
## 1934 2020-10-10 South East 3
## 1935 2020-10-11 South East 3
## 1936 2020-10-12 South East 4
## 1937 2020-10-13 South East 2
## 1938 2020-10-14 South East 2
## 1939 2020-10-15 South East 3
## 1940 2020-10-16 South East 2
## 1941 2020-10-17 South East 3
## 1942 2020-10-18 South East 4
## 1943 2020-10-19 South East 7
## 1944 2020-10-20 South East 8
## 1945 2020-10-21 South East 9
## 1946 2020-10-22 South East 5
## 1947 2020-10-23 South East 7
## 1948 2020-10-24 South East 5
## 1949 2020-10-25 South East 9
## 1950 2020-10-26 South East 13
## 1951 2020-10-27 South East 10
## 1952 2020-10-28 South East 10
## 1953 2020-10-29 South East 7
## 1954 2020-10-30 South East 6
## 1955 2020-10-31 South East 15
## 1956 2020-11-01 South East 18
## 1957 2020-11-02 South East 13
## 1958 2020-11-03 South East 16
## 1959 2020-11-04 South East 10
## 1960 2020-11-05 South East 10
## 1961 2020-11-06 South East 16
## 1962 2020-11-07 South East 17
## 1963 2020-11-08 South East 18
## 1964 2020-11-09 South East 19
## 1965 2020-11-10 South East 20
## 1966 2020-11-11 South East 20
## 1967 2020-11-12 South East 20
## 1968 2020-11-13 South East 12
## 1969 2020-11-14 South East 24
## 1970 2020-11-15 South East 25
## 1971 2020-11-16 South East 22
## 1972 2020-11-17 South East 23
## 1973 2020-11-18 South East 26
## 1974 2020-11-19 South East 21
## 1975 2020-11-20 South East 18
## 1976 2020-11-21 South East 23
## 1977 2020-11-22 South East 30
## 1978 2020-11-23 South East 29
## 1979 2020-11-24 South East 26
## 1980 2020-11-25 South East 42
## 1981 2020-11-26 South East 30
## 1982 2020-11-27 South East 31
## 1983 2020-11-28 South East 24
## 1984 2020-11-29 South East 37
## 1985 2020-11-30 South East 23
## 1986 2020-12-01 South East 29
## 1987 2020-12-02 South East 33
## 1988 2020-12-03 South East 36
## 1989 2020-12-04 South East 41
## 1990 2020-12-05 South East 36
## 1991 2020-12-06 South East 32
## 1992 2020-12-07 South East 25
## 1993 2020-12-08 South East 43
## 1994 2020-12-09 South East 44
## 1995 2020-12-10 South East 38
## 1996 2020-12-11 South East 48
## 1997 2020-12-12 South East 40
## 1998 2020-12-13 South East 41
## 1999 2020-12-14 South East 38
## 2000 2020-12-15 South East 50
## 2001 2020-12-16 South East 45
## 2002 2020-12-17 South East 54
## 2003 2020-12-18 South East 48
## 2004 2020-12-19 South East 42
## 2005 2020-12-20 South East 57
## 2006 2020-12-21 South East 66
## 2007 2020-12-22 South East 62
## 2008 2020-12-23 South East 70
## 2009 2020-12-24 South East 56
## 2010 2020-12-25 South East 71
## 2011 2020-12-26 South East 75
## 2012 2020-12-27 South East 76
## 2013 2020-12-28 South East 81
## 2014 2020-12-29 South East 78
## 2015 2020-12-30 South East 91
## 2016 2020-12-31 South East 91
## 2017 2021-01-01 South East 60
## 2018 2021-01-02 South East 94
## 2019 2021-01-03 South East 78
## 2020 2021-01-04 South East 105
## 2021 2021-01-05 South East 105
## 2022 2021-01-06 South East 121
## 2023 2021-01-07 South East 113
## 2024 2021-01-08 South East 123
## 2025 2021-01-09 South East 114
## 2026 2021-01-10 South East 122
## 2027 2021-01-11 South East 128
## 2028 2021-01-12 South East 166
## 2029 2021-01-13 South East 128
## 2030 2021-01-14 South East 136
## 2031 2021-01-15 South East 125
## 2032 2021-01-16 South East 149
## 2033 2021-01-17 South East 161
## 2034 2021-01-18 South East 146
## 2035 2021-01-19 South East 152
## 2036 2021-01-20 South East 133
## 2037 2021-01-21 South East 126
## 2038 2021-01-22 South East 128
## 2039 2021-01-23 South East 118
## 2040 2021-01-24 South East 115
## 2041 2021-01-25 South East 112
## 2042 2021-01-26 South East 130
## 2043 2021-01-27 South East 110
## 2044 2021-01-28 South East 127
## 2045 2021-01-29 South East 104
## 2046 2021-01-30 South East 89
## 2047 2021-01-31 South East 90
## 2048 2021-02-01 South East 84
## 2049 2021-02-02 South East 74
## 2050 2021-02-03 South East 77
## 2051 2021-02-04 South East 28
## 2052 2021-02-05 South East 11
## 2053 2020-03-01 South West 0
## 2054 2020-03-02 South West 0
## 2055 2020-03-03 South West 0
## 2056 2020-03-04 South West 0
## 2057 2020-03-05 South West 0
## 2058 2020-03-06 South West 0
## 2059 2020-03-07 South West 0
## 2060 2020-03-08 South West 0
## 2061 2020-03-09 South West 0
## 2062 2020-03-10 South West 0
## 2063 2020-03-11 South West 1
## 2064 2020-03-12 South West 0
## 2065 2020-03-13 South West 0
## 2066 2020-03-14 South West 1
## 2067 2020-03-15 South West 0
## 2068 2020-03-16 South West 0
## 2069 2020-03-17 South West 2
## 2070 2020-03-18 South West 2
## 2071 2020-03-19 South West 4
## 2072 2020-03-20 South West 3
## 2073 2020-03-21 South West 6
## 2074 2020-03-22 South West 7
## 2075 2020-03-23 South West 8
## 2076 2020-03-24 South West 7
## 2077 2020-03-25 South West 9
## 2078 2020-03-26 South West 11
## 2079 2020-03-27 South West 13
## 2080 2020-03-28 South West 21
## 2081 2020-03-29 South West 18
## 2082 2020-03-30 South West 23
## 2083 2020-03-31 South West 23
## 2084 2020-04-01 South West 21
## 2085 2020-04-02 South West 23
## 2086 2020-04-03 South West 30
## 2087 2020-04-04 South West 42
## 2088 2020-04-05 South West 32
## 2089 2020-04-06 South West 34
## 2090 2020-04-07 South West 39
## 2091 2020-04-08 South West 47
## 2092 2020-04-09 South West 24
## 2093 2020-04-10 South West 46
## 2094 2020-04-11 South West 43
## 2095 2020-04-12 South West 23
## 2096 2020-04-13 South West 27
## 2097 2020-04-14 South West 24
## 2098 2020-04-15 South West 32
## 2099 2020-04-16 South West 29
## 2100 2020-04-17 South West 33
## 2101 2020-04-18 South West 25
## 2102 2020-04-19 South West 31
## 2103 2020-04-20 South West 26
## 2104 2020-04-21 South West 26
## 2105 2020-04-22 South West 23
## 2106 2020-04-23 South West 17
## 2107 2020-04-24 South West 19
## 2108 2020-04-25 South West 15
## 2109 2020-04-26 South West 27
## 2110 2020-04-27 South West 13
## 2111 2020-04-28 South West 17
## 2112 2020-04-29 South West 15
## 2113 2020-04-30 South West 26
## 2114 2020-05-01 South West 6
## 2115 2020-05-02 South West 7
## 2116 2020-05-03 South West 10
## 2117 2020-05-04 South West 17
## 2118 2020-05-05 South West 14
## 2119 2020-05-06 South West 19
## 2120 2020-05-07 South West 16
## 2121 2020-05-08 South West 6
## 2122 2020-05-09 South West 11
## 2123 2020-05-10 South West 5
## 2124 2020-05-11 South West 8
## 2125 2020-05-12 South West 7
## 2126 2020-05-13 South West 7
## 2127 2020-05-14 South West 6
## 2128 2020-05-15 South West 4
## 2129 2020-05-16 South West 4
## 2130 2020-05-17 South West 6
## 2131 2020-05-18 South West 4
## 2132 2020-05-19 South West 6
## 2133 2020-05-20 South West 1
## 2134 2020-05-21 South West 9
## 2135 2020-05-22 South West 7
## 2136 2020-05-23 South West 6
## 2137 2020-05-24 South West 3
## 2138 2020-05-25 South West 8
## 2139 2020-05-26 South West 11
## 2140 2020-05-27 South West 5
## 2141 2020-05-28 South West 10
## 2142 2020-05-29 South West 7
## 2143 2020-05-30 South West 3
## 2144 2020-05-31 South West 2
## 2145 2020-06-01 South West 7
## 2146 2020-06-02 South West 2
## 2147 2020-06-03 South West 7
## 2148 2020-06-04 South West 2
## 2149 2020-06-05 South West 2
## 2150 2020-06-06 South West 1
## 2151 2020-06-07 South West 3
## 2152 2020-06-08 South West 3
## 2153 2020-06-09 South West 0
## 2154 2020-06-10 South West 1
## 2155 2020-06-11 South West 2
## 2156 2020-06-12 South West 2
## 2157 2020-06-13 South West 2
## 2158 2020-06-14 South West 0
## 2159 2020-06-15 South West 2
## 2160 2020-06-16 South West 2
## 2161 2020-06-17 South West 0
## 2162 2020-06-18 South West 0
## 2163 2020-06-19 South West 0
## 2164 2020-06-20 South West 2
## 2165 2020-06-21 South West 0
## 2166 2020-06-22 South West 1
## 2167 2020-06-23 South West 1
## 2168 2020-06-24 South West 1
## 2169 2020-06-25 South West 0
## 2170 2020-06-26 South West 3
## 2171 2020-06-27 South West 0
## 2172 2020-06-28 South West 0
## 2173 2020-06-29 South West 1
## 2174 2020-06-30 South West 0
## 2175 2020-07-01 South West 0
## 2176 2020-07-02 South West 0
## 2177 2020-07-03 South West 0
## 2178 2020-07-04 South West 0
## 2179 2020-07-05 South West 1
## 2180 2020-07-06 South West 0
## 2181 2020-07-07 South West 0
## 2182 2020-07-08 South West 2
## 2183 2020-07-09 South West 0
## 2184 2020-07-10 South West 1
## 2185 2020-07-11 South West 0
## 2186 2020-07-12 South West 0
## 2187 2020-07-13 South West 1
## 2188 2020-07-14 South West 0
## 2189 2020-07-15 South West 0
## 2190 2020-07-16 South West 0
## 2191 2020-07-17 South West 1
## 2192 2020-07-18 South West 0
## 2193 2020-07-19 South West 0
## 2194 2020-07-20 South West 0
## 2195 2020-07-21 South West 0
## 2196 2020-07-22 South West 0
## 2197 2020-07-23 South West 0
## 2198 2020-07-24 South West 0
## 2199 2020-07-25 South West 0
## 2200 2020-07-26 South West 0
## 2201 2020-07-27 South West 0
## 2202 2020-07-28 South West 0
## 2203 2020-07-29 South West 0
## 2204 2020-07-30 South West 1
## 2205 2020-07-31 South West 0
## 2206 2020-08-01 South West 0
## 2207 2020-08-02 South West 0
## 2208 2020-08-03 South West 0
## 2209 2020-08-04 South West 0
## 2210 2020-08-05 South West 0
## 2211 2020-08-06 South West 0
## 2212 2020-08-07 South West 0
## 2213 2020-08-08 South West 0
## 2214 2020-08-09 South West 0
## 2215 2020-08-10 South West 0
## 2216 2020-08-11 South West 0
## 2217 2020-08-12 South West 0
## 2218 2020-08-13 South West 0
## 2219 2020-08-14 South West 1
## 2220 2020-08-15 South West 0
## 2221 2020-08-16 South West 0
## 2222 2020-08-17 South West 2
## 2223 2020-08-18 South West 0
## 2224 2020-08-19 South West 0
## 2225 2020-08-20 South West 0
## 2226 2020-08-21 South West 0
## 2227 2020-08-22 South West 0
## 2228 2020-08-23 South West 0
## 2229 2020-08-24 South West 0
## 2230 2020-08-25 South West 1
## 2231 2020-08-26 South West 0
## 2232 2020-08-27 South West 1
## 2233 2020-08-28 South West 0
## 2234 2020-08-29 South West 0
## 2235 2020-08-30 South West 0
## 2236 2020-08-31 South West 0
## 2237 2020-09-01 South West 0
## 2238 2020-09-02 South West 0
## 2239 2020-09-03 South West 0
## 2240 2020-09-04 South West 0
## 2241 2020-09-05 South West 0
## 2242 2020-09-06 South West 0
## 2243 2020-09-07 South West 0
## 2244 2020-09-08 South West 1
## 2245 2020-09-09 South West 0
## 2246 2020-09-10 South West 0
## 2247 2020-09-11 South West 0
## 2248 2020-09-12 South West 0
## 2249 2020-09-13 South West 1
## 2250 2020-09-14 South West 0
## 2251 2020-09-15 South West 0
## 2252 2020-09-16 South West 0
## 2253 2020-09-17 South West 1
## 2254 2020-09-18 South West 0
## 2255 2020-09-19 South West 0
## 2256 2020-09-20 South West 1
## 2257 2020-09-21 South West 0
## 2258 2020-09-22 South West 0
## 2259 2020-09-23 South West 0
## 2260 2020-09-24 South West 1
## 2261 2020-09-25 South West 0
## 2262 2020-09-26 South West 0
## 2263 2020-09-27 South West 0
## 2264 2020-09-28 South West 0
## 2265 2020-09-29 South West 0
## 2266 2020-09-30 South West 0
## 2267 2020-10-01 South West 0
## 2268 2020-10-02 South West 1
## 2269 2020-10-03 South West 0
## 2270 2020-10-04 South West 0
## 2271 2020-10-05 South West 0
## 2272 2020-10-06 South West 1
## 2273 2020-10-07 South West 0
## 2274 2020-10-08 South West 1
## 2275 2020-10-09 South West 1
## 2276 2020-10-10 South West 0
## 2277 2020-10-11 South West 4
## 2278 2020-10-12 South West 2
## 2279 2020-10-13 South West 0
## 2280 2020-10-14 South West 3
## 2281 2020-10-15 South West 1
## 2282 2020-10-16 South West 2
## 2283 2020-10-17 South West 8
## 2284 2020-10-18 South West 2
## 2285 2020-10-19 South West 2
## 2286 2020-10-20 South West 3
## 2287 2020-10-21 South West 6
## 2288 2020-10-22 South West 6
## 2289 2020-10-23 South West 5
## 2290 2020-10-24 South West 5
## 2291 2020-10-25 South West 5
## 2292 2020-10-26 South West 7
## 2293 2020-10-27 South West 6
## 2294 2020-10-28 South West 8
## 2295 2020-10-29 South West 11
## 2296 2020-10-30 South West 8
## 2297 2020-10-31 South West 4
## 2298 2020-11-01 South West 5
## 2299 2020-11-02 South West 11
## 2300 2020-11-03 South West 7
## 2301 2020-11-04 South West 8
## 2302 2020-11-05 South West 5
## 2303 2020-11-06 South West 11
## 2304 2020-11-07 South West 10
## 2305 2020-11-08 South West 10
## 2306 2020-11-09 South West 12
## 2307 2020-11-10 South West 6
## 2308 2020-11-11 South West 13
## 2309 2020-11-12 South West 17
## 2310 2020-11-13 South West 9
## 2311 2020-11-14 South West 8
## 2312 2020-11-15 South West 16
## 2313 2020-11-16 South West 18
## 2314 2020-11-17 South West 17
## 2315 2020-11-18 South West 26
## 2316 2020-11-19 South West 15
## 2317 2020-11-20 South West 25
## 2318 2020-11-21 South West 25
## 2319 2020-11-22 South West 24
## 2320 2020-11-23 South West 14
## 2321 2020-11-24 South West 20
## 2322 2020-11-25 South West 25
## 2323 2020-11-26 South West 16
## 2324 2020-11-27 South West 21
## 2325 2020-11-28 South West 35
## 2326 2020-11-29 South West 15
## 2327 2020-11-30 South West 21
## 2328 2020-12-01 South West 19
## 2329 2020-12-02 South West 15
## 2330 2020-12-03 South West 14
## 2331 2020-12-04 South West 20
## 2332 2020-12-05 South West 17
## 2333 2020-12-06 South West 13
## 2334 2020-12-07 South West 15
## 2335 2020-12-08 South West 19
## 2336 2020-12-09 South West 21
## 2337 2020-12-10 South West 20
## 2338 2020-12-11 South West 20
## 2339 2020-12-12 South West 15
## 2340 2020-12-13 South West 19
## 2341 2020-12-14 South West 20
## 2342 2020-12-15 South West 20
## 2343 2020-12-16 South West 9
## 2344 2020-12-17 South West 26
## 2345 2020-12-18 South West 11
## 2346 2020-12-19 South West 22
## 2347 2020-12-20 South West 19
## 2348 2020-12-21 South West 21
## 2349 2020-12-22 South West 10
## 2350 2020-12-23 South West 16
## 2351 2020-12-24 South West 18
## 2352 2020-12-25 South West 19
## 2353 2020-12-26 South West 24
## 2354 2020-12-27 South West 24
## 2355 2020-12-28 South West 20
## 2356 2020-12-29 South West 20
## 2357 2020-12-30 South West 14
## 2358 2020-12-31 South West 26
## 2359 2021-01-01 South West 29
## 2360 2021-01-02 South West 24
## 2361 2021-01-03 South West 27
## 2362 2021-01-04 South West 30
## 2363 2021-01-05 South West 31
## 2364 2021-01-06 South West 24
## 2365 2021-01-07 South West 29
## 2366 2021-01-08 South West 33
## 2367 2021-01-09 South West 26
## 2368 2021-01-10 South West 31
## 2369 2021-01-11 South West 36
## 2370 2021-01-12 South West 48
## 2371 2021-01-13 South West 40
## 2372 2021-01-14 South West 32
## 2373 2021-01-15 South West 44
## 2374 2021-01-16 South West 54
## 2375 2021-01-17 South West 32
## 2376 2021-01-18 South West 42
## 2377 2021-01-19 South West 43
## 2378 2021-01-20 South West 62
## 2379 2021-01-21 South West 54
## 2380 2021-01-22 South West 57
## 2381 2021-01-23 South West 52
## 2382 2021-01-24 South West 59
## 2383 2021-01-25 South West 55
## 2384 2021-01-26 South West 41
## 2385 2021-01-27 South West 39
## 2386 2021-01-28 South West 48
## 2387 2021-01-29 South West 41
## 2388 2021-01-30 South West 35
## 2389 2021-01-31 South West 34
## 2390 2021-02-01 South West 27
## 2391 2021-02-02 South West 33
## 2392 2021-02-03 South West 28
## 2393 2021-02-04 South West 20
## 2394 2021-02-05 South West 51.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 04 Feb 2021.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 16 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 16 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.585 -13.944 -4.764 7.975 34.872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.944e+00 7.003e-02 70.60 <2e-16 ***
## note_lag 1.347e-05 1.153e-06 11.69 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 177.1799)
##
## Null deviance: 64564 on 287 degrees of freedom
## Residual deviance: 47089 on 286 degrees of freedom
## (16 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 140.357070 1.000013
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 122.024539 160.588110
## note_lag 1.000011 1.000016
Rsq(lag_mod)
## [1] 0.2706597
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64"
## nodename
## "Mac-1612691082651.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.5 ggpubr_0.4.0 lubridate_1.7.9.2
## [4] chngpt_2020.10-12 cyphr_1.1.0 DT_0.17
## [7] kableExtra_1.3.1 janitor_2.1.0 remotes_2.2.0
## [10] projections_0.5.2 earlyR_0.0.5 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.3 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.1 stringr_1.4.0
## [22] dplyr_1.0.4 purrr_0.3.4 readr_1.4.0
## [25] tidyr_1.1.2 tibble_3.0.6 ggplot2_3.3.3
## [28] tidyverse_1.3.0 here_1.0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_2.0-0 selectr_0.4-2 ggsignif_0.6.0
## [5] ellipsis_0.3.1 rprojroot_2.0.2 snakecase_0.11.0 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.0.3 fansi_0.4.2 splines_4.0.3
## [13] knitr_1.31 jsonlite_1.7.2 nloptr_1.2.2.2 broom_0.7.4
## [17] dbplyr_2.1.0 compiler_4.0.3 httr_1.4.2 backports_1.2.1
## [21] assertthat_0.2.1 Matrix_1.2-18 cli_2.3.0 htmltools_0.5.1.1
## [25] tools_4.0.3 gtable_0.3.0 glue_1.4.2 Rcpp_1.0.6
## [29] carData_3.0-4 cellranger_1.1.0 vctrs_0.3.6 nlme_3.1-149
## [33] matchmaker_0.1.1 crosstalk_1.1.1 xfun_0.20 ps_1.5.0
## [37] openxlsx_4.2.3 lme4_1.1-26 lifecycle_0.2.0 statmod_1.4.35
## [41] rstatix_0.6.0 MASS_7.3-53 scales_1.1.1 hms_1.0.0
## [45] parallel_4.0.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [49] gridExtra_2.3 stringi_1.5.3 highr_0.8 kyotil_2020.10-12
## [53] boot_1.3-25 zip_2.1.1 rlang_0.4.10 pkgconfig_2.0.3
## [57] evaluate_0.14 lattice_0.20-41 labeling_0.4.2 htmlwidgets_1.5.3
## [61] cowplot_1.1.1 tidyselect_1.1.0 plyr_1.8.6 magrittr_2.0.1
## [65] R6_2.5.0 generics_0.1.0 DBI_1.1.1 pillar_1.4.7
## [69] haven_2.3.1 foreign_0.8-80 withr_2.4.1 mgcv_1.8-33
## [73] survival_3.2-7 abind_1.4-5 modelr_0.1.8 crayon_1.4.0
## [77] car_3.0-10 utf8_1.1.4 rmarkdown_2.6 viridis_0.5.1
## [81] grid_4.0.3 readxl_1.3.1 data.table_1.13.6 reprex_1.0.0
## [85] digest_0.6.27 webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0